CFP last date
20 May 2024
Reseach Article

Matching and Retrieval of Near Duplicate Images

Published on March 2017 by Vishakha B. Pawar, J. R. Mankar
Emerging Trends in Computing
Foundation of Computer Science USA
ETC2016 - Number 3
March 2017
Authors: Vishakha B. Pawar, J. R. Mankar
94bd5c1b-1bde-42d4-8532-9a42b09c8432

Vishakha B. Pawar, J. R. Mankar . Matching and Retrieval of Near Duplicate Images. Emerging Trends in Computing. ETC2016, 3 (March 2017), 10-13.

@article{
author = { Vishakha B. Pawar, J. R. Mankar },
title = { Matching and Retrieval of Near Duplicate Images },
journal = { Emerging Trends in Computing },
issue_date = { March 2017 },
volume = { ETC2016 },
number = { 3 },
month = { March },
year = { 2017 },
issn = 0975-8887,
pages = { 10-13 },
numpages = 4,
url = { /proceedings/etc2016/number3/27315-6269/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Emerging Trends in Computing
%A Vishakha B. Pawar
%A J. R. Mankar
%T Matching and Retrieval of Near Duplicate Images
%J Emerging Trends in Computing
%@ 0975-8887
%V ETC2016
%N 3
%P 10-13
%D 2017
%I International Journal of Computer Applications
Abstract

Using some transformation on original images, images can be modified which forms near duplicate images. Using the size and length of image, image represented and the length is variable with number of patches in the image. Spatially adjacent and similar pixels are used to form clusters. Patch means a form of clusters. Image representation and image similarity measurement are two major issues in image matching. The proposed method extracts patches from given image and represents by variable length signature. In the detection of duplicate natural images the signatures are use. Then take a decision for images are duplicates or not. The retrieval of near duplicate images means a query image and datasets are given then the near duplicate images are retrieving. Similarity is computed between two images, which can be done on variable length signature to improve the effectiveness.

References
  1. Li Liu, Yue Lu, Senior Member, IEEE, and Ching Y. Suen,Fellow,Variable-Length Signature for Near-Duplicate ImageMatching, IEEE NO. 4, APRIL 2015.
  2. F. Zou et al. , Nonnegative sparse coding induced hashing forimage copy detection, Neurocomputing, vol. 105, no. 1, pp. 81-89, 2013.
  3. G. -H. Liu and J. -Y. Yang,Content-based image retrieval usingcolor difference histogram, Pattern Recognit. , vol. 46, no. 1,pp. 188-198, 2013.
  4. Y. Lu, X. Tu, S. Lu, and P. S. P. Wang, Application of patternrecognition technology to postal automation in China,IEEEPattern Recognition and Machine Vision-in Honor andMemory of Professor King-Sun Fu. Copenhagen, Denmark: River Pub. Co. , Mar. 2010, pp. 367-381.
  5. S. Todorovic and N. Ahuja, Region-based hierarchical imagematching," Int. J. Comput. Vis. , vol. 78, no. 1, pp. 47-66, 2008.
  6. O. Chum, J. Philbin, M. Isard, and A. Zisserman, Scalablenear identical image and shot detection,in Proc. 6th ACM Int. Conf. Image Video Retr. , 2007, pp. 549-556.
  7. B. Wang, Z. Li, M. Li, and W. -Y. Ma,Large-scale duplicatedetection for web image search," in Proc. IEEE Int. Conf. Multimedia Expo, Jul. 2006, pp. 353-356.
  8. D. -Q. Zhang and S. -F. Chang,Detecting image near-duplicateby stochastic attributed relational graph matching with learning,in Proc. ACM Int. Conf. Multimedia,2004, pp. 877-884.
  9. Y. Meng, E. Chang, and B. Li,Enhancing DPF for near-replicaimage recognition,in Proc. Int. Conf. Comput. Vis. Pattern Recognit. , Jun. 2003, pp. II-416-II-423.
  10. C. Kim,Content-based image copy detection,Signal Process. ,Image Commun. ,vol. 18, no. 3, pp. 169-184, 2003.
Index Terms

Computer Science
Information Sciences

Keywords

Image Retrieval Near Duplicate Image Similarity Matching Variable Length Signature.